Functional Projection <i>K</i>-means حسب Roberto Rocci (16022201)

"…In contrast to existing literature, which largely considers the smoothing as a pre-processing step, in our proposal regularization is integrated with the identification of both subspace and cluster partition. An alternating least squares algorithm is introduced to compute model parameter estimates. …"

Efficient Algorithms for GPU Accelerated Evaluation of the DFT Exchange-Correlation Functional حسب Ryan Stocks (16867476)

"…Kohn–Sham density functional theory (KS-DFT) has become a cornerstone for studying the electronic structure of molecules and materials. …"

Decoding results. حسب Sam Hall-McMaster (10343795)

Behavioral results. حسب Sam Hall-McMaster (10343795)

Pictorial representation of isomorphically scaling down spots within a fixed field of view and corresponding imaging configuration. حسب Nicholas H. Vitale (20469289)

A detailed process of iterative simulation coupled with bone density algorithm; (a) a function of stimulus and related bone density changes, and (b) iterative calculations of finit... حسب Hassan Mehboob (8960273)

The pseudocode for the NAFPSO algorithm. حسب Huichao Guo (14515171)

"…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …"

PSO algorithm flowchart. حسب Huichao Guo (14515171)

"…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …"

The traffic flow distribution diagram when traffic lights in both entrance and exit of the short-distance road section are red. حسب Yulin Tian (1457986)

The traffic flow distribution diagram when traffic lights in both entrance and exit of the short-distance road section are green. حسب Yulin Tian (1457986)

Algorithm comparison experiment on the NWPU VHR-10 dataset. حسب Pingping Yan (462509)

الموضوعات:

Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information. حسب Yuanchen Zhao (12905580)

"…Whiskers show the lowest and highest values within 1.5 × <i>IQR</i> from the first and third quartiles, respectively. …"

Classification algorithm implemented in SPARTA. حسب Baptiste Ruiz (20285217)

Geometric interpretation and data visualisation equivalent describing the DI algorithmic pipeline. حسب Borjan Milinkovic (21349908)

Code snippets to add user-specific custom functionality in PyNoetic. حسب Gursimran Singh (575288)

الموضوعات:

The benchmark functions. حسب Shuhui Yu (1535500)

Gillespie algorithm simulation parameters. حسب Nicholas H. Vitale (20469289)

"…Both the ensemble and stochastic models presented in this work have been verified using Monte Carlo molecular dynamic simulations that utilize the Gillespie algorithm. …"

Scheduling time of five algorithms. حسب Huichao Guo (14515171)

"…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …"

Convergence speed of five algorithms. حسب Huichao Guo (14515171)

"…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …"

Elementary effects (EE) results for the generation of villous vessels within a placentone employing the input parameter ranges from Table 1. حسب Diana C. de Oliveira (9220729)